Search and exploration using analytics reference model

ABSTRACT

Searching and exploration using a data-driven analytics model. The analytics model includes an analytical modeling component that defines analytical relationships between model variables using a number of analytical relations. In response to a search request, the output variable(s) of the solve operation are identified. The output variable(s) may have even been identified based on the search request. The analytical relations of the model may then be used to solve for the identified output variable(s). The resulting value(s) for the now solved-for output variable(s) may then be used to formulate the response to the search request. The nature of the response may vary depending on the scope of the application that embodied the search request capability. The results of the search request may be used for further exploration of the model by, for example, submitting follow-up search requests, resulting in follow-up solve operations.

BACKGROUND

Often, the most effective way to convey information to a human being isvisually. Accordingly, millions of people work with a wide range ofvisual items in order to convey or receive information, and in order tocollaborate. Such visual items might include, for example, conceptsketches, engineering drawings, explosions of bills of materials,three-dimensional models depicting various structures such as buildingsor molecular structures, training materials, illustrated installationinstructions, planning diagrams, and so on.

More recently, these visual items are constructed electronically using,for example, Computer Aided Design (CAD) and solid modelingapplications. Often these applications allow authors to attach data andconstraints to the geometry. For instance, the application forconstructing a bill of materials might allow for attributes such as partnumber and supplier to be associated with each part, the maximum anglebetween two components, or the like. An application that constructs anelectronic version of an arena might have a tool for specifying aminimum clearance between seats, and so on.

Such applications have contributed enormously to the advancement ofdesign and technology. However, any given application does have limitson the type information that can be visually conveyed, how thatinformation is visually conveyed, or the scope of data and behavior thatcan be attributed to the various visual representations. If theapplication is to be modified to go beyond these limits, a newapplication would typically be authored by a computer programmer whichexpands the capabilities of the application, or provides an entirely newapplication. Also, there are limits to how much a user (other than theactual author of the model) can manipulate the model to test variousscenarios.

BRIEF SUMMARY

Embodiments described herein relate to searching using a data-drivenanalytics model. The analytics model includes an analytical modelingcomponent that defines analytical relationships between model variablesusing a number of analytical relations. For example, such analyticalrelations might be equations, rules, constraints, simulations, or anyother analytical relationship between model variables. For perhaps eachsolve operation, the analytics model identifies which of the modelvariables is or are to be solved for, and which are input variable(s).In one embodiment, the identity of the output and input modelvariable(s) may change from one solve operation to the next.

In response to a search request, the output variable(s) of the solveoperation are identified. The output variable(s) may have even beenidentified based on the search request. In one embodiment, values forone or more of the input variable(s) may also be derived in preparationfor the solve operation based on information provided in the searchrequest. The analytical relations of the model may then be used to solvefor the identified output variable(s). The resulting value(s) for thenow solved-for output variable(s) may then be used to formulate theresponse to the search request. The nature of the response may varydepending on the scope of the application that embodied the searchrequest capability.

The results of the search request may be used for further exploration ofthe as model by, for example, submitting follow-up search requests. Thefollow-up search request may, perhaps, result in another solve operationusing the analytical relations. In this case, the output variable(s)solved for in the next solve operation may be the same or different thanthe output variable(s) for the prior solve operation. In fact, thesolved-for value for the prior search request may be used for the nextsearch request. These effects result in a rich search mechanism forexploring model analytics.

This Summary is not intended to identify key features or essentialfeatures of the claimed subject matter, nor is it intended to be used asan aid in determining the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features can be obtained, a more particular descriptionof various embodiments will be rendered by reference to the appendeddrawings. Understanding that these drawings depict only sampleembodiments and are not therefore to be considered to be limiting of thescope of the invention, the embodiments will be described and explainedwith additional specificity and detail through the use of theaccompanying drawings in which:

FIG. 1 illustrates an environment in which the principles of the presentinvention may be employed including a data-driven composition frameworkthat constructs a view composition that depends on input data;

FIG. 2 illustrates a pipeline environment that represents one example ofthe environment of FIG. 1;

FIG. 3 schematically illustrates an embodiment of the data portion ofthe pipeline of FIG. 2;

FIG. 4 schematically illustrates an embodiment of the analytics portionof the pipeline of FIG. 2;

FIG. 5 schematically illustrates an embodiment of the view portion ofthe pipeline of FIG. 2;

FIG. 6 illustrates a rendering of a view composition that may beconstructed by the pipeline of FIG. 2;

FIG. 7 illustrates a flowchart of a method for generating a viewcomposition using the pipeline environment of FIG. 2;

FIG. 8 illustrates a flowchart of a method for regenerating a viewcomposition in response to user interaction with the view compositionusing the pipeline environment of FIG. 2;

FIG. 9 schematically illustrates the solver of the analytics portion ofFIG. 4 in further detail including a collection of specialized solvers;

FIG. 10 illustrates a flowchart of the solver of FIG. 9 solving forunknown model parameters by coordinating the actions of a collection ofspecialized solvers;

FIG. 11 schematically illustrates a solver environment that representsan example of the solver of FIG. 9;

FIG. 12 illustrates a flowchart of a method for using the solverenvironment of FIG. 11 to solve for model analytics;

FIG. 13 illustrates a rendering of an integrated view composition thatextends the example of FIG. 6;

FIG. 14 illustrates a visualization of a shelf layout and representsjust one of countless applications that the principles described hereinmay apply to;

FIG. 15 illustrates a visualization of an urban plan that the principlesdescribed herein may also apply to;

FIG. 16 illustrates a conventional visualization comparing children'seducation, that the principles of the present invention may apply tothereby creating a more dynamic learning environment;

FIG. 17 illustrates a conventional visualization comparing populationdensity, that the principles of the present invention may apply tothereby creating a more dynamic learning environment;

FIG. 18 illustrates a taxonomy environment in which the taxonomycomponent of FIG. 2 may operate;

FIG. 19 illustrates an example of a taxonomy of the member items of FIG.18;

FIGS. 20A through 20C show three examples of taxonomies of relatedcategories;

FIG. 21 illustrates a member item that includes multiple properties;

FIG. 22 illustrates a domain-specific taxonomy and represents oneexample of the domain-specified taxonomies of FIG. 18;

FIG. 23 illustrates a flowchart of a method for navigating and usinganalytics;

FIG. 24 illustrates a flowchart of a method for searching usinganalytics; and

FIG. 25 illustrates a computing system that represents an environment inwhich the composition framework of FIG. 1 (or portions thereof) may beimplemented.

DETAILED DESCRIPTION

FIG. 1 illustrates a visual composition environment 100 that usesdata-driven analytics and visualization of the analytical results. Theenvironment 100 (also called hereinafter a “pipeline”) includes acomposition framework 110 that performs logic that is performedindependent of the problem-domain of the view construction 130. Forinstance, the same composition framework 110 may be used to composeinteractive view compositions for city plans, molecular models, groceryshelf layouts, machine performance or assembly analysis, or otherdomain-specific renderings. As will be described, the analytics may beused to search and explore in various scenarios. First, however, thebasic composition framework 110 will be described in detail.

The composition framework 110 uses domain-specific data 120 that istaxonomically organized in a domain-specific way to construct the actualvisual construction 130 that is specific to the domain. Accordingly, thesame composition framework 110 may be used to construct viewcompositions for any number of different domains by changing thedomain-specific data 120, rather than having to recode the compositionframework 110 itself. Thus, the composition framework 110 of thepipeline 100 may apply to a potentially unlimited number of problemdomains, or at least to a wide variety of problem domains, by alteringdata, rather than recoding and recompiling. The view construction 130may then be supplied as instructions to an appropriate 2-D or 3-Drendering module. The architecture described herein also allows forconvenient incorporation of pre-existing view compositions as buildingblocks to new view compositions. In one embodiment, multiple viewcompositions may be included in an integrated view composition to allowfor easy comparison between two possible solutions to a model.

FIG. 2 illustrates an example architecture of the composition framework110 in the form of a pipeline environment 200. The pipeline environment200 includes, amongst other things, the pipeline 201 itself The pipeline201 includes a data portion 210, an analytics portion 220, and a viewportion 230, which will each be described in detail with respect tosubsequent FIGS. 3 through 5, respectively, and the accompanyingdescription. For now, at a general level, the data portion 210 of thepipeline 201 may accept a variety of different types of data andpresents that data in a canonical form to the analytics portion 220 ofthe pipeline 201. The analytics portion 220 binds the data to variousmodel parameters, and solves for the unknowns in the model parametersusing model analytics. The various parameter values are then provided tothe view portion 230, which constructs the composite view using thosevalues of the model parameters.

The pipeline environment 200 also includes an authoring component 240that allows an author or other user of the pipeline 201 to formulateand/or select data to provide to the pipeline 201. For instance, theauthoring component 240 may be used to supply data to each of dataportion 210 (represented by input data 211), analytics portion 220(represented by analytics data 221), and view portion 230 (representedby view data 231). The various data 211, 221 and 231 represent anexample of the domain-specific data 120 of FIG. 1, and will be describedin much further detail hereinafter. The authoring component 240 supportsthe providing of a wide variety of data including for example, dataschemas, actual data to be used by the model, the location or range ofpossible locations of data that is to be brought in from externalsources, visual (graphical or animation) objects, user interfaceinteractions that can be performed on a visual, modeling statements(e.g., views, equations, constraints), bindings, and so forth.

In one embodiment, the authoring component is but one portion of thefunctionality provided by an overall manager component (not shown inFIG. 2, but represented by the composition framework 110 of FIG. 1). Themanager is an overall director that controls and sequences the operationof all the other components (such as data connectors, solvers, viewers,and so forth) in response to events (such as user interaction events,external data events, and events from any of the other components suchas the solvers, the operating system, and so forth).

The authoring component 240 also includes a search tool 242 that allowsfor searches to be performed. The search tool 242 is able to draw on thedata-driven analytical capabilities of the pipeline 201 in order toperform complex search operations. For instance, in some cases, one ormore parameters of the search might first need to be solved for in orderto complete the search.

As an example, suppose that the data driven analytics is a city map, andsome of the analytics are capable of solving for the typical noise levelat particular coordinates in the city. In that case, a person searchingfor residential real estate may perform a search not just on the typicalsearch parameters of square footage, price range, number of rooms, andso forth, but may also search on analytically intensive parameters. Forinstance, while there are unlimited ways that the principles may beapplied, a selected few diverse examples of how a flexible search forreal estate might be accomplished will now be provided.

As part of specifying search parameters, the user might indicate adesire for any one or more of the following or other search items:

-   -   1) only those homes that experience average noise levels below a        certain upper limit;    -   2) only those homes that have a 30 minute commute time or less        to the user's work on each of Monday through Thursday,    -   3) only those homes that experience less than a certain        threshold of traffic on any road within one fifth of a mile and        which are predicted over the next 10 years to remain below that        level of traffic,    -   4) only those homes that are not in the shadow of a mountain        after 9:15 am at any point in the year,    -   5) only those homes that have sufficient existing trees on the        property such that in 10 years time, the trees would shade at        least 50% of the area of the roof,    -   6) and so forth.        Such real estate searches are not readily achievable using        conventional technology. In each of these examples, the        requested home search data may not exist, but the principles        described herein may allow the search data for a variety of        customized parameters to be generated on the fly as the search        is being performed. Also, the search may take advantage of any        search data that has been previously solved for. This enables a        wide variety of search and exploration capabilities for the        user, and opens up a whole new way of exploring a problem to be        solved. More regarding the underlying data-driven analytics        mechanisms that support these types of searches will now be        described.

Traditionally, the lifecycle of an interactive view compositionapplication involves two key times: authoring time, and use time. Atauthoring time, the functionality of the interactive view compositionapplication is coded by a programmer to provide an interactive viewcomposition that is specific to the desired domain. For instance, theauthor of an interior design application (e.g., typically, a computerprogrammer) might code an application that permits a user to perform afinite set of actions specific to interior designing.

At use time, a user (e.g., perhaps a home owner or a professionalinterior designer) might then use the application to perform any one ormore of the set of finite actions that are hard coded into theapplication. In the interior design application example, the user mightspecify the dimensions of a virtual room being displayed, add furnitureand other interior design components to the room, perhaps rotate theview to get various angles on the room, set the color of each item, andso forth. However, unless the user is a programmer that does not mindreverse-engineering and modifying the interior design application, theuser is limited to the finite set of actions that were enabled by theapplication author. For example, unless offered by the application, theuser would not be able to use the application to automatically figureout which window placement would minimize ambient noise, how the roomlayout performs according to Feng Shui rules, or minimize solar heatcontribution.

However, in the pipeline environment 200 of FIG. 2, the authoringcomponent 240 is used to provide data to an existing pipeline 201, whereit is the data that drives the entire process from defining the inputdata, to defining the analytical model, to defining how the results ofthe analytics are visualized in the view composition. Accordingly, oneneed not perform any coding in order to adapt the pipeline 201 to anyone of a wide variety of domains and problems. Only the data provided tothe pipeline 201 is what is to change in order to apply the pipeline 201to visualize a different view composition either from a differentproblem domain altogether, or to perhaps adjust the problem solving foran existing domain. The pipeline environment 200 may also include ataxonomy component 260 that organizes, categorizes, and relates datathat is provided to the pipeline 201. The taxonomy component 260 may besensitive to the domain. Thus, an interior design domain, a road designdomain, an architecture domain, a Feng Shui domain may each have adifferent taxonomy that may be used to navigate through the data. Thiscan be helpful since, as will be described below, there may beconsiderable data available to sift through due to composition activityin which the data set that is made available to the pipeline environment200 may be ever increasing.

Further, since the data can be changed at use time (i.e., run time), aswell as at author time, the model can be modified and/or extended atruntime. Thus, there is less, if any, distinction between authoring amodel and running the model. Because all authoring involves editing dataitems and because the software runs all of its behavior from data, everychange to data immediately affects behavior without the need forrecoding and recompilation.

The pipeline environment 200 also includes a user interaction responsemodule 250 that detects when a user has interacted with the displayedview composition, and then determines what to do in response. Forexample, some types of interactions might require no change in the dataprovided to the pipeline 201 and thus require no change to the viewcomposition. Other types of interactions may change one or more of thedata 211, 221, or 231. In that case, this new or modified data may causenew input data to be provided to the data portion 210, might require areanalysis of the input data by the analytics portion 220, and/or mightrequire a re-visualization of the view composition by the view portion230.

Accordingly, the pipeline 201 may be used to extend data-drivenanalytical visualizations to perhaps an unlimited number of problemdomains, or at least to a wide variety of problem domains. Furthermore,one need not be a programmer to alter the view composition to address awide variety of problems. Each of the data portion 210, the analyticsportion 220 and the view portion 230 of the pipeline 201 will now bedescribed with respect to the data portion 300 of FIG. 3, the analyticsportion 400 of FIG. 4, and the view portion 500 of FIG. 5, in thatorder. In addition, the taxonomy of the data is specific to the domain,thus allowing the organization of the data to be more intuitive to thoseoperating in the domain. As will be apparent from FIGS. 3 through 5, thepipeline 201 may be constructed as a series of transformation componentswhere they each 1) receive some appropriate input data, 2) perform someaction in response to that input data (such as performing atransformation on the input data), and 3) output data which then servesas input data to the next transformation component.

The pipeline 201 may be implemented on the client, on the server, or mayeven be distributed amongst the client and the server withoutrestriction. For instance, the pipeline 201 might be implemented on theserver and provide rendering instructions as output. A browser at theclient-side may then just render according to the rendering instructionsreceived from the server. At the other end of the spectrum, the pipeline201 may be contained on the client with authoring and/or use performedat the client. Even if the pipeline 201 was entirely at the client, thepipeline 201 might still search data sources external to the client forappropriate information (e.g., models, connectors, canonicalizers,schemas, and others). There are also embodiments that provide a hybridof these two approaches. For example, in one such hybrid approach, themodel is hosted on a server but web browser modules are dynamicallyloaded on the client so that some of the model's interaction and viewinglogic is made to run on the client (thus allowing richer and fasterinteractions and views).

FIG. 3 illustrates just one of many possible embodiments of a dataportion 300 of the pipeline 201 of FIG. 2. One of the functions of thedata portion 300 is to provide data in a canonical format that isconsistent with schemas understood by the analytics portion 400 of thepipeline discussed with respect to FIG. 4. The data portion includes adata access component 310 that accesses the heterogenic data 301. Theinput data 301 may be “heterogenic” in the sense that the data may (butneed not) be presented to the data access component 310 in a canonicalform. In fact, the data portion 300 is structured such that theheterogenic data could be of a wide variety of formats. Examples ofdifferent kinds of domain data that can be accessed and operated on bymodels include text and XML documents, tables, lists, hierarchies(trees), SQL database query results, BI (business intelligence) cubequery results, graphical information such as 2D drawings and 3D visualmodels in various formats, and combinations thereof (i.e., a composite).Further, the kind of data that can be accessed can be extendeddeclaratively, by providing a definition (e.g., a schema) for the datato be accessed. Accordingly, the data portion 300 permits a wide varietyof heterogenic input into the model, and also supports runtime,declarative extension of accessible data types.

In one embodiment, the data access portion 300 includes a number ofconnectors for obtaining data from a number of different data sources.Since one of the primary functions of the connector is to placecorresponding data into canonical form, such connectors will often bereferred to hereinafter and in the drawings as “canonicalizers”. Eachcanonicalizer might have an understanding of the specific ApplicationProgram Interfaces (API's) of its corresponding data source. Thecanonicalizer might also include the corresponding logic for interfacingwith that corresponding API to read and/or write data from and to thedata source. Thus, canonicalizers bridge between external data sourcesand the memory image of the data.

The data access component 310 evaluates the input data 301. If the inputdata is already canonical and thus processable by the analytics portion400, then the input data may be directly provided as canonical data 340to be input to the analytics portion 400.

However, if the input data 301 is not canonical, then the appropriatedata canonicalization component 330 is able to convert the input data301 into the canonical format. The data canonicalization components 330are actually a collection of data canonicalization components 330, eachcapable of converting input data having particular characteristics intocanonical form. The collection of canonicalization components 330 isillustrated as including four canonicalization components 331, 332, 333and 334. However, the ellipsis 335 represents that there may be othernumbers of canonicalization components as well, perhaps even fewer thatthe four illustrated.

The input data 301 may even include a canonicalizer itself as well as anidentification of correlated data characteristic(s). The data portion300 may then register the correlated data characteristics, and providethe canonicalization component to the data canonicalization componentcollection 330, where it may be added to the available canonicalizationcomponents. If input data is later received that has those correlatedcharacteristics, the data portion 310 may then assign the input data tothe correlated canonicalization component. Canonicalization componentscan also be found dynamically from external sources, such as fromdefined component libraries on the web. For example, if the schema for agiven data source is known but the needed canonicalizer is not present,the canonicalizer can be located from an external component library,provided such a library can be found and contains the needed components.The pipeline might also parse data for which no schema is yet known andcompare parse results versus schema information in known componentlibraries to attempt a dynamic determination of the type of the data,and thus to locate the needed canonicalizer components.

Alternatively, instead of the input data including all of thecanonicalization components, the input data may instead provide atransformation definition defining canonicalization transformations. Thecollection 330 may then be configured to convert that transformationsdefinition into a corresponding canonicalization component that enforcesthe transformations along with zero or more standard defaultcanonicalization transformation. This represents an example of a case inwhich the data portion 300 consumes the input data and does not providecorresponding canonicalized data further down the pipeline. In perhapsmost cases, however, the input data 301 results in correspondingcanonicalized data 340 being generated.

In one embodiment, the data portion 310 may be configured to assigninput data to the data canonicalization component on the basis of a filetype and/or format type of the input data. Other characteristics mightinclude, for example, the source of the input data. A defaultcanonicalization component may be assigned to input data that does nothave a designated corresponding canonicalization component. The defaultcanonicalization component may apply a set of rules to attempt to ascanonicalize the input data. If the default canonicalization componentis not able to canonicalize the data, the default canonicalizationcomponent might trigger the authoring component 240 of FIG. 2 to promptthe user to provide a schema definition for the input data. If a schemadefinition does not already exist, the authoring component 240 mightpresent a schema definition assistant to help the author generate acorresponding schema definition that may be used to transform the inputdata into canonical form. Once the data is in canonical form, the schemathat accompanies the data provides sufficient description of the datathat the rest of the pipeline 201 does not need new code to interpretthe data. Instead, the pipeline 201 includes code that is able tointerpret data in light of any schema that is expressible an accessibleschema declaration language.

Regardless, canonical data 340 is provided as output data from the dataportion 300 and as input data to the analytics portion 400. Thecanonical data might include fields that include a variety of datatypes. For instance, the fields might include data types such asintegers, floating point numbers, strings, vectors, arrays, collections,hierarchical structures, text, XML documents, tables, lists, SQLdatabase query results, BI (business intelligence) cube query results,graphical information such as 2D drawings and 3D visual models invarious formats, or even complex combinations of these various datatypes. As another advantage, the canonicalization process is able tocanonicalize a wide variety of input data. Furthermore, the variety ofinput data that the data portion 300 is able to accept is expandable.This is helpful in the case where multiple models are combined as willbe discussed later in this description.

FIG. 4 illustrates analytics portion 400 which represents an example ofthe analytics portion 220 of the pipeline 201 of FIG. 2. The dataportion 300 provided the canonicalized data 401 to the data-modelbinding component 410. The canonicalized data 401 might have anycanonicalized form, and any number of parameters, where the form andnumber of parameters might even differ from one piece of input data toanother. For purposes of discussion, however, the canonical data 401 hasfields 402A through 402H, which may collectively be referred to hereinas “fields 402”.

On the other hand, the analytics portion 400 includes a number of modelparameters 411. The type and number of model parameters may differaccording to the model. However, for purposes of discussion of aparticular example, the model parameters 411 will be discussed asincluding model parameters 411A, 411B, 411C and 411D. In one embodiment,the identity of the model parameters, and the analytical relationshipsbetween the model parameters may be declaratively defined without usingimperative coding.

A data-model binding component 410 intercedes between the canonicalizeddata fields 402 and the model parameters 411 to thereby provide bindingsbetween the fields. In this case, the data field 402B is bound to modelparameter 411A as represented by arrow 403A. In other words, the valuefrom data field 402B is used to populate the model parameter 411A. Also,in this example, the data field 402E is bound to model parameter 411B(as represented by arrow 403B), and data field 402H is bound to modelparameter 411C (as represented by arrow 403C).

The data fields 402A, 402C, 402D, 402F and 402G are not shown bound toany of the model parameters. This is to emphasize that not all of thedata fields from input data are always required to be used as modelparameters. In one embodiment, one or more of these data fields may beused to provide instructions to the data-model binding component 410 onwhich fields from the canonicalized data (for this canonicalized data orperhaps any future similar canonicalized data) are to be bound to whichmodel parameter. This represents an example of the kind of analyticsdata 221 that may be provided to the analytics portion 220 of FIG. 2.The definition of which data fields from the canonicalized data arebound to which model parameters may be formulated in a number of ways.For instance, the bindings may be 1) explicitly set by the author atauthoring time, 2) explicitly set by the user at use time (subject toany restrictions imposed by the author), 3) automatic binding by theauthoring component 240 based on algorithmic heuristics, and/or 4)prompting by the authoring component of the author and/or user tospecify a binding when it is determined that a binding cannot be madealgorithmically. Thus bindings may also be resolved as part of the modellogic itself.

The ability of an author to define which data fields are mapped to whichmodel parameters gives the author great flexibility in being able to usesymbols that the author is comfortable with to define model parameters.For instance, if one of the model parameters represents pressure, theauthor can name that model parameter “Pressure” or “P” or any othersymbol that makes sense to the author. The author can even rename themodel parameter which, in one embodiment, might cause the data modelbinding component 410 to automatically update to allow bindings thatwere previously to the model parameter of the old name to instead bebound to the model parameter of the new name, thereby preserving thedesired bindings. This mechanism for binding also allows binding to bechanged declaratively at runtime.

The model parameter 411D is illustrated with an asterisk to emphasizethat in this example, the model parameter 411D was not assigned a valueby the data-model binding component 410. Accordingly, the modelparameter 411D remains an unknown. In other words, the model parameter411D is not assigned a value.

The modeling component 420 performs a number of functions. First, themodeling component 420 defines analytical relationships 421 between themodel parameters 411. The analytical relationships 421 are categorizedinto three general categories including equations 431, rules 432 andconstraints 433. However, the list of solvers is extensible. In oneembodiment, for example, one or more simulations may be incorporated aspart of the analytical relationships provided a corresponding simulationengine is provided and registered as a solver.

The term “equation” as used herein aligns with the term as it is used inthe field of mathematics.

The term “rules” as used herein means a conditional statement where ifone or more conditions are satisfied (the conditional or “if” portion ofthe conditional statement), then one or more actions are to be taken(the consequence or “then” portion of the conditional statement). A ruleis applied to the model parameters if one or more model parameters areexpressed in the conditional statement, or one or more model parametersare expressed in the consequence statement.

The term “constraint” as used herein means that a restriction is appliedto one or more model parameters. For instance, in a city planning model,a particular house element may be restricted to placement on a maplocation that has a subset of the total possible zoning designations. Abridge element may be restricted to below a certain maximum length, or acertain number of lanes.

An author that is familiar with the model may provide expressions ofthese equations, rules and constraint that apply to that model. In thecase of simulations, the author might provide an appropriate simulationengine that provides the appropriate simulation relationships betweenmodel parameters. The modeling component 420 may provide a mechanism forthe author to provide a natural symbolic expression for equations, rulesand constraints. For example, an author of a thermodynamics relatedmodel may simply copy and paste equations from a thermodynamicstextbook. The ability to bind model parameters to data fields allows theauthor to use whatever symbols the author is familiar with (such as theexact symbols used in the author's relied-upon textbooks) or the exactsymbols that the author would like to use.

Prior to solving, the modeling component 420 also identifies which ofthe model parameters are to be solved for (i.e., hereinafter, the“output model variable” if singular, or “output model variables” ifplural, or “output model variable(s)” if there could be a single orplural output model variables). The output model variables may beunknown parameters, or they might be known model parameters, where thevalue of the known model parameter is subject to change in the solveoperation. In the example of FIG. 4, after the data-model bindingoperation, model parameters 411A, 411B and 411C are known, and modelparameter 411D is unknown. Accordingly, unknown model parameter 411Dmight be one of the output model variables. Alternatively or inaddition, one or more of the known model parameters 411A, 411B and 411Cmight also be output model variables. The solver 440 then solves for theoutput model variable(s), if possible. In one embodiment describedhereinafter, the solver 440 is able to solve for a variety of outputmodel variables, even within a single model so long as sufficient inputmodel variables are provided to allow the solve operation to beperformed. Input model variables might be, for example, known modelparameters whose values are not subject to change during the solveoperation. For instance, in FIG. 4, if the model parameters 411A and411D were input model variables, the solver might instead solve foroutput model variables 411B and 411C instead. In one embodiment, thesolver might output any one of a number of different data types for asingle model parameter. For instance, some equation operations (such asaddition, subtraction, and the like) apply regardless of the whether theoperands are integers, floating point, vectors of the same, or matricesof the same.

In one embodiment, even when the solver 440 cannot solve for aparticular output model variable, the solver 400 might still present apartial solution for that output model variable, even if a full solve tothe actual numerical result (or whatever the solved-for data type) isnot possible. This allows the pipeline to facilitate incrementaldevelopment by prompting the author as to what information is needed toarrive at a full solve. This also helps to eliminate the distinctionbetween author time and use time, since at least a partial solve isavailable throughout the various authoring stages. For an abstractexample, suppose that the analytics model includes an equation a=b+c+d.Now suppose that a, c and d are output model variables, and b is aninput model variable having a known value of 5 (an integer in thiscase). In the solving process, the solver 440 is only able to solve forone of the output model variables “d”, and assign a value of 6 (aninteger) to the model parameter called “d”, but the solver 440 is notable to solve for “c”. Since “a” depends from “c”, the model parametercalled “a” also remains an unknown and unsolved for. In this case,instead of assigning an integer value to “a”, the solver might do apartial solve and output the string value of “c+11” to the modelparameter “a”. As previously mentioned, this might be especially helpfulwhen a domain expert is authoring an analytics model, and willessentially serve to provide partial information regarding the contentof model parameter “a” and will also serve to cue the author that somefurther model analytics needs to be provided that allow for the “c”model parameter to be solved for. This partial solve result may beperhaps output in some fashion in the view composition to allow thedomain expert to see the partial result.

The solver 440 is shown in simplified form in FIG. 4. However, thesolver 440 may direct the operation of multiple constituent solvers aswill be described with respect to FIGS. 9 through 12. In FIG. 4, themodeling component 420 then makes the model parameters 411 (includingthe now known and solved-for output model variables) available as outputto be provided to the view portion 500 of FIG. 5.

FIG. 5 illustrates a view portion 500 which represents an example of theview portion 230 of FIG. 2. The view portion 500 receives the modelparameters 411 from the analytics portion 400 of FIG. 4. The viewportion also includes a view components repository 520 that contains acollection of view components. For example, the view componentsrepository 520 in this example is illustrated as including viewcomponents 521 through 524, although the view components repository 520may contain any number of view components. The view components each mayinclude zero or more input parameters. For example, view component 521does not include any input parameters. However, view component 522includes two input parameters 542A and 542B. View component 523 includesone input parameter 543, and view component 524 includes one inputparameter 544. That said, this is just an example. The input parametersmay, but need not necessarily, affect how the visual item is rendered.The fact that the view component 521 does not include any inputparameters emphasizes that there can be views that are generated withoutreference to any model parameters. Consider a view that comprises justfixed (built-in) data that does not change. Such a view might forexample constitute reference information for the user. Alternatively,consider a view that just provides a way to browse a catalog, so thatitems can be selected from it for import into a model.

Each view component 521 through 524 includes or is associated withcorresponding logic that, when executed by the view compositioncomponent 540 using the corresponding view component input parameter(s),if any, causes a corresponding view item to be placed in virtual space550. That virtual item may be a static image or object, or may be adynamic animated virtual item or object For instance, each of viewcomponents 521 through 524 are associated with corresponding logic 531through 534 that, when executed causes the corresponding virtual item551 through 554, respectively, to be rendered in virtual space 550. Thevirtual items are illustrated as simple shapes. However, the virtualitems may be quite complex in form perhaps even including animation. Inthis description, when a view item is rendered in virtual space, thatmeans that the view composition component has authored sufficientinstructions that, when provided to the rendering engine, the renderingengine is capable of displaying the view item on the display in thedesignated location and in the designated manner.

The view components 521 through 524 may be provided perhaps even as viewdata to the view portion 500 using, for example, the authoring component240 of FIG. 2. For instance, the authoring component 240 might provide aselector that enables the author to select from several geometric forms,or perhaps to compose other geometric forms. The author might alsospecify the types of input parameters for each view component, whereassome of the input parameters may be default input parameters imposed bythe view portion 500. The logic that is associated with each viewcomponent 521 through 524 may be provided with view data, and/or mayalso include some default functionality provided by the view portion 500itself

The view portion 500 includes a model-view binding component 5 10 thatis configured to bind at least some of the model parameters tocorresponding input parameters of the view components 521 through 524.For instance, model parameter 411A is bound to the input parameter 542Aof view component 522 as represented by arrow 511A. Model parameter 411Bis bound to the input parameter 542B of view component 522 asrepresented by arrow 511B. Also, model parameter 411D is bound to theinput parameters 543 and 544 of view components 523 and 524,respectively, as represented by arrow 511C. The model parameter 411C isnot shown bound to any corresponding view component parameter,emphasizing that not all model parameters need be used by the viewportion of the pipeline, even if those model parameters were essentialin the analytics portion. Also, the model parameter 411D is shown boundto two different input parameters of view components representing thatthe model parameters may be bound to multiple view component parameters.In one embodiment, the definition of the bindings between the modelparameters and the view component parameters may be formulated by 1)being explicitly set by the author at authoring time, 2) explicitly setby the user at use time (subject to any restrictions imposed by theauthor), 3) automatic binding by the authoring component 240 based onalgorithmic heuristics, and/or 4) prompting by the authoring componentof the author and/or user to specify a binding when it is determinedthat a binding cannot be made algorithmically.

As previously mentioned, the view item may include an animation. To takea simple example, consider for example a bar chart that plots acompany's historical and projected revenues, advertising expenses, andprofits by sales region at a given point in time (such as a givencalendar quarter). A bar chart could be drawn for each calendar quarterin a desired time span. Now, imagine that you draw one of these charts,say the one for the earliest time in the time span, and then every halfsecond replace it with the chart for the next time span (e.g., the nextquarter). The result will be to see the bars representing profit, sales,and advertising expense for each region change in height as theanimation proceeds. In this example, the chart for each time period is a“cell” in the animation, where the cell shows an instant betweenmovements, where the collection of cells shown in sequence simulatesmovement. Conventional animation models allow for animation over timeusing built-in hard-coded chart types.

However, using the pipeline 201, by contrast, any kind of visual can beanimated, and the animation can be driven by varying any one or anycombination of the parameters of the visual component. To return to thebar chart example above, imagine that instead of animating by time, weanimate by advertising expense. Each “cell” in this animation is a barchart showing sales and profits over time for a given value ofadvertising expense. Thus, as the advertising expense is varied, thebars grow and shrink in response to the change in advertising expense.

The power of animated data displays is that they make very apparent tothe eye what parameters are most sensitive to change in otherparameters, because you immediately see how quickly and how far eachparameter's values change in response to the varying of the animationparameter.

The pipeline 201 is also distinguished in its ability to animate due tothe following characteristics:

First, the sequences of steps for the animation variable can be computedby the analytics of the model, versus being just a fixed sequence ofsteps over a predefined range. For example, in the example of varyingthe advertising expense as the animation variable, imagine that what isspecified is to “animate by advertising expense where advertisingexpense is increased by 5% for each step” or “where advertising expenseis 10% of total expenses for that step”. A much more sophisticatedexample is “animate by advertising expense where advertising expense isoptimized to maximize the rate of change of sales over time”. In otherwords, the solver will determine a set of steps for advertising spendover time (i.e., for each successive time period such as quarter) suchthat the rate of growth of sales is maximized. Here the user presumablywants to see not only how fast sales can be made to grow by varyingadvertising expense, but also wants to learn the quarterly amounts forthe advertising expenses that achieve this growth (the sequence ofvalues could be plotted as part of the composite visual).

Second, any kind of visual can be animated, not just traditional datacharts. For example, consider a Computer-Aided Design (CAD) model of ajet engine that is a) to be animated by the air speed parameter and 2)where the rotational speed of the turbine is a function of the air speedand 3) where the temperature of the turbine bearings is a function ofthe air speed. Jet engines have limits on how fast turbines can berotated before either the turbine blades lose integrity or the bearingoverheats. Thus, in this animation we desire that as air speed is variedthe color of the turbine blades and bearing should be varied from blue(safe) to red (critical). The values for “safe” and “critical” turbineRPM and bearing temperature may well be calculated by the model based onphysical characteristics of those parts. Now, as the animation variesthe air speed over a defined range, we see the turbine blades andbearing each change color. What is now interesting is to notice whichreaches critical first, and if either undergoes a sudden (runaway) runto critical. These kinds of effects are hard to discern by looking at achart or at a sequence of drawings, but become immediately apparent inan animation. This is but one example of animating an arbitrary visual(CAD model) by an arbitrary parameter (air speed), with the animationaffecting yet other arbitrary parameters (turbine RPM and bearing temp).Any parameter(s) of any visual(s) can be animated according to anydesired parameter(s) that are to serve as the animation variables.

Third, the pipeline 201 can be stopped mid stream so that data andparameters may be modified by the user, and the animation then restartedor resumed. Thus, for example, in the jet engine example, if runawayheating is seen to start at a given air speed, the user may stop theanimation at the point the runaway beings, modify some engine designcriterion, such as the kind of bearing or bearing surface material, andthen continue the animation to see the effect of the change.

As with other of the capabilities discussed herein, animations can bedefined by the author, and/or left open for the user to manipulate totest various scenarios. For example, the model may be authored to permitsome visuals to be animated by the user according to parameters the userhimself selects, and/or over data ranges for the animation variable thatthe user selects (including the ability to specify computed rangesshould that be desired). Such animations can also be displayed side byside as in the other what-if comparison displays. For example, a usercould compare an animation of sales and profits over time, animated bytime, in two scenarios with differing prevailing interest rates in thefuture, or different advertising expenses ramps. In the jet engineexample, the user could compare the animations of the engine for boththe before and after cases of changing the bearing design.

At this point, a specific example of how the composition framework maybe used to actually construct a view composition will be described withrespect to FIG. 6, which illustrated 3-D renderings 600 of a viewcomposition that includes a room layout 601 with furniture laid outwithin the room, and also includes a Feng Shui meter 602. This exampleis provided merely to show how the principles described herein can applyto any arbitrary view composition, regardless of the domain.Accordingly, the example of FIG. 6, and any other example viewcomposition described herein, should be viewed strictly as only anexample that allows the abstract concept to be more fully understood byreference to non-limiting concrete examples, and not defining thebroader scope of the invention. The principles described herein mayapply to construct a countless variety of view compositions.Nevertheless, reference to a concrete example can clarif the broaderabstract principles.

FIG. 7 illustrates a flowchart of a method 700 for generating a viewconstruction. The method 700 may be performed by the pipelineenvironment 200 of FIG. 2, and thus will be described with frequentreference to the pipeline environment 200 of FIG. 2, as well as withreference to FIGS. 3 through 5, which each show specific portions of thepipeline of FIG. 2. While the method 700 may be performed to constructany view composition, the method 700 will be described with respect tothe view composition 600 of FIG. 6. Some of the acts of the method 700may be performed by the data portion 210 of FIG. 2 and are listed in theleft column of FIG. 7 under the header “Data”. Other of the acts of themethod 700 may be performed by the analytics portion 220 of FIG. 2, andare listed in the second from the left column of FIG. 7 under the header“Analytics”. Other of the acts of the method may be performed by theview portion 230 of FIG. 2, and are listed in the second from the rightcolumn under the header “View”. One of the acts may be performed by arendering module and is listed in the right column under the headerother. Any conventional or yet to be developed rendering module may beused to render a view composition constructed in accordance with theprinciples described herein.

Referring to FIG. 7, the data portion accesses input data that at leastcollectively affects what visual items are displayed or how a given oneor more of the visual items are displayed (act 711). For instance,referring to FIG. 6, the input data might include view components foreach of the items of furniture. For instance, each of the couch, thechair, the plants, the table, the flowers, and even the room itself maybe represented by a corresponding view component. The view componentmight have input parameters that are suitable for the view component. Ifanimation were employed, for example, some of the input parameters mightaffect the flow of the animation. Some of the parameters might affectthe display of the visual item, and some parameters might not.

For instance, the room itself might be a view component. Some of theinput parameters might include the dimensions of the room, theorientation of the room, the wall color, the wall texture, the floorcolor, the floor type, the floor texture, the position and power of thelight sources in the room, and so forth. There might also be roomparameters that do not necessarily get reflected in this viewcomposition, but might get reflected in other views and uses of the roomcomponent. For instance, the room parameter might have a location of theroom expressed in degrees, minutes, and seconds longitude and latitude.The room parameter might also include an identification of the author ofthe room component, and the average rental costs of the room.

The various components within the room may also be represented by acorresponding parameterized view component. For instance, each plant maybe configured with an input parameter specifying a pot style, a potcolor, pot dimensions, plant color, plant resiliency, plant dependencieson sunlight, plant daily water intake, plant daily oxygen production,plant position and the like. Once again, some of these parameters mayaffect how the display is rendered and others might not, depending onthe nature of what is being displayed.

The Feng Shui meter 602 may also be a view component. The meter mightinclude input parameters such as a diameter, a number of wedges to becontained in the diameter of the meter, a text color and the like. Thevarious wedges of the Feng Shui meter may also be view components. Inthat case, the input parameters to the view components might be a title(e.g., water, mountain, thunder, wind, fire, earth, lake, heaven),perhaps a graphic to appear in the wedge, a color hue, or the like.

The analytics portion binds the input data to the model parameters (act721), determines the output model variables (act 722), and uses themodel-specific analytical relationships between the model parameters tosolve for the output model variables (act 723). The binding operation ofact 721 has been previously discussed, and essentially allowsflexibility in allowing the author to define the model analyticsequations, rules and constraints using symbols that the model author iscomfortable with. The more complex solver described with respect toFIGS. 9 through 12 may serve to solve for the output model variables(act 723).

The identification of the output model variables may differ from onesolving operation to the next. Even though the model parameters may staythe same, the identification of which model parameters are output modelvariables will depend on the availability of data to bind to particularmodel parameters. This has remarkable implications in terms of allowinga user to perform what-if scenarios in a given view composition.

For instance, in the Feng Shui room example of FIG. 6, suppose the userhas bought a new chair to place in their living room. The user mightprovide the design of the room as data into the pipeline. This might befacilitated by the authoring component prompting the user to enter theroom dimensions, and perhaps provide a selection tool that allows theuser to select virtual furniture to drag and drop into the virtual roomat appropriate locations that the actual furniture is placed in theactual room. The user might then select a piece of furniture that may beedited to have the characteristics of the new chair purchased by theuser. The user might then drag and drop that chair into the room. TheFeng Shui meter 602 would update automatically. In this case, theposition and other attributes of the chair would be input modelvariables, and the Feng Shui scores would be output model variables. Asthe user drags the virtual chair to various positions, the Feng Shuiscores of the Feng Shui meter would update, and the user could thus testthe Feng Shui consequences of placing the virtual chair in variouslocations. To avoid the user from having to drag the chair to everypossible location to see which gives the best Feng Shui, the user canget local visual clues (such as, for example, gradient lines or arrows)that tell the user whether moving the chair in a particular directionfrom its current location makes things better or worse, and how muchbetter or worse.

However, the user could also do something else that is unheard of inconventional view composition. The user could actually change the outputmodel variables. For instance, the user might indicate the desired FengShui score in the Feng Shui meter, and leave the position of the virtualchair as the output model variable. The solver would then solve for theoutput model variable and provide a suggested position or positions ofthe chair that would achieve at least the designated Feng Shui score.The user may choose to make multiple parameters output model variables,and the system may provide multiple solutions to the output modelvariables. This is facilitated by a complex solver that is described infurther detail with respect to FIGS. 9 through 12.

Returning to FIG. 7, once the output model variables are solved for, themodel parameters are bound to the input parameters of the parameterizedview components (act 731). For instance, in the Feng Shui example, afterthe unknown Feng Shui scores are solved for, the scores are bound asinput parameters to Feng Shui meter view component, or perhaps to theappropriate wedge contained in the meter. Alternatively, if the FengShui scores were input model variables, the position of the virtualchair may be solved for and provided as an input parameter to the chairview component.

A simplified example will now be presented that illustrates theprinciples of how the solver can rearrange equations and change thedesignation of input and output model variables all driven off of oneanalytical model. The user herself does not have to rearrange theequations. The simplified example may not accurately represent Feng Shuirules, but illustrates the principle nevertheless. Suppose the totalFeng Shui (FS) of the room (FSroom) equals the FS of a chair (FSchair)and the FS of a plant (FSplant). Suppose FSchair is equal to a constantA times the distance d of the chair from the wall. Suppose FSplant is aconstant, B. The total FS of the room is then: FSroom=A*d+B. If d is aninput model variable, then FSroom is an output model variable and itsvalue, displayed on the meter, changes as the user repositions thechair. Now suppose the user clicks on the meter making it an input modelvariable and shifting d into unknown output model variable status. Inthis case, the solver effectively and internally rewrites the equationabove as d=(FSroom−B)/A. In that case, the view component can move thechair around, changing d, its distance from the wall, as the userchanges the desired value, FSroom, on the meter.

The view portion then constructs a view of the visual items (act 732) byexecuting the construction logic associated with the view componentusing the input parameter(s), if any, to perhaps drive the constructionof the view item in the view composition. The view construction may thenbe provided to a rendering module, which then uses the view constructionas rendering instructions (act 741).

In one embodiment, the process of constructing a view is treated as adata transformation that is performed by the solver. That is, for agiven kind of view (e.g., consider a bar chart), there is a modelconsisting of rules, equations, and constraints that generates the viewby transforming the input data into a displayable output data structure(called a scene graph) which encodes all the low level geometry andassociated attributes needed by the rendering software to drive thegraphics hardware. In the bar chart example, the input data would be forexample the data series that is to be plotted, along with attributes forthings like the chart title, axis labels, and so on. The model thatgenerates the bar would have rules, equations, and constraints thatwould do things like 1) count how many entries the data series consistsof in order to determine how many bars to draw, 2) calculate the range(min, max) that the data series spans in order to calculate things likethe scale and starting/ending values for each axis, 3) calculate theheight of the bar for each data point in the data series based on thepreviously calculated scale factor, 4) count how many characters are inthe chart title in order to calculate a starting position and size forthe title so that the title will be properly located and centered withrespect to the chart, and so forth. In sum, the model is designed tocalculate a set of geometric shapes based on the input data, with thosegeometric shapes arranged within a hierarchical data structure of type“scene graph”. In other words, the scene graph is an output variablethat the model solves for based on the input data. Thus, an author candesign entirely new kinds of views, customize existing views, andcompose preexisting views into composites, using the same framework thatthe author uses to author, customize, and compose any kind of model.Thus, authors who are not programmers can create new views withoutdrafting new code.

Returning to FIG. 2, recall that the user interaction response module250 detects when the user interacts with the view composition, andcauses the pipeline to respond appropriately. FIG. 8 illustrates aflowchart of a method 800 for responding to user interaction with theview composition. In particular, the user interaction response moduledetermines which the components of the pipeline should perform furtherwork in order to regenerate the view, and also provides data representedthe user interaction, or that is at least dependent on the userinteraction, to the pipeline components. In one embodiment, this is donevia a transformation pipeline that runs in the reverse (upstream)view/analytics/data direction and is parallel to the (downstream)data/analytics/view pipeline.

Interactions are posted as events into the upstream pipeline. Eachtransformer in the data/analytics/view pipeline provides an upstreamtransformer that handles incoming interaction data. These transformerscan either be null (passthroughs, which get optimized out of the path)or they can perform a transformation operation on the interaction datato be fed further upstream. This provides positive performance andresponsiveness of the pipeline in that 1) interaction behaviors thatwould have no effect on upstream transformations, such as a viewmanipulation that has no effect on source data, can be handled at themost appropriate (least upstream) point in the pipeline and 2)intermediate transformers can optimize view update performance bysending heuristically-determined updates back downstream, ahead of thefinal updates that will eventually come from further upstreamtransformers. For example, upon receipt of a data edit interaction, aview-level transformer could make an immediate view update directly intothe scene graph for the view (for edits it knows how to interpret), withthe final complete update coming later from the upstream datatransformer where the source data is actually edited.

When the semantics of a given view interaction have a nontrivial mappingto the needed underlying data edits, intermediate transformers canprovide the needed upstream mapping. For example, dragging a point on agraph of a computed result could require a backwards solve that wouldcalculate new values for multiple source data items that feed thecomputed value on the graph. The solver-level upstream transformer wouldbe able to invoke the needed solve and to propagate upstream the neededdata edits.

FIG. 8 illustrates a flowchart of a method 800 for responding to userinteraction with the view construction. Upon detecting that the user hasinteracted with the rendering of a view composition on the display (act801), it is first determined whether or not the user interactionrequires regeneration of the view (decision block 802). This may beperformed by the rendering engine raising an event that is interpretedby the user interaction response component 250 of FIG. 2. If the userinteraction does not require regeneration of the view (No in decisionblock 802), then the pipeline does not perform any further action toreconstruct the view (act 803), although the rendering engine itself mayperform some transformation on the view. An example of such a userinteraction might be if the user were to increase the contrast of therendering of the view construction, or rotate the view construction.Since those actions might be undertaken by the rendering engine itself,the pipeline need perform no work to reconstruct the view in response tothe user interaction.

If, on the other hand, it is determined that the type of userinteraction does require regeneration of the view construction (Yes indecision block 802), the view is reconstructed by the pipeline (act804). This may involve some altering of the data provided to thepipeline. For instance, in the Feng Shui example, suppose the user wereto move the position of the virtual chair within the virtual room, theposition parameter of the virtual chair component would thus change. Anevent would be fired informing the analytics portion that thecorresponding model parameter representing the position of the virtualchair should be altered as well. The analytics component would thenresolve for the Feng Shui scores, repopulate the corresponding inputparameters of the Feng Shui meter or wedges, causing the Feng Shui meterto update with current Feng Shui scores suitable for the new position ofthe chair.

The user interaction might require that model parameters that werepreviously known are now unknown, and that previously unknown parametersare now known. That is one of several possible examples that mightrequire a change in designation of input and output model variables suchthat previously designated input model variables might become outputmodel variables, and vice versa. In that case, the analytics portionwould solve for the new output model variable(s) thereby driving thereconstruction of the view composition.

Solver Framework

FIG. 9 illustrates a solver environment 900 that may represent anexample of the solver 440 of FIG. 4. The solver environment 900 may beimplemented in software, hardware, or a combination. The solverenvironment 900 includes a solver framework 901 that manages andcoordinates the operations of a collection 910 of specialized solvers.The collection 910 is illustrated as including three specialized solvers911, 912 and 913, but the ellipsis 914 represents that there could beother numbers (i.e., more than three or less than three) of specializedsolvers as well. Furthermore, the ellipsis 914 also represents that thecollection 910 of specialized solves is extensible. As new specializedsolvers are discovered and/or developed that can help with the modelanalytics, those new specialized solvers may be incorporated into thecollection 910 to supplement the existing specialized solvers, orperhaps to replace one or more of the existing solvers. For example,FIG. 9 illustrates that a new solver 915 is being registered into thecollection 910 using the solver registration module 921. As one example,a new solver might be perhaps a simulation solver which accepts one ormore known values, and solves for one or more unknown values. Otherexamples include solvers for systems of linear equations, differentialequations, polynomials, integrals, root-finders, factorizers,optimizers, and so forth. Every solver can work in numerical mode or insymbolic mode or in mixed numeric-symbolic mode. The numeric portions ofsolutions can drive the parameterized rendering downstream. The symbolicportions of the solution can drive partial solution rendering.

The collection of specialized solvers may include any solver that issuitable for solving for the output model variables. If, for example,the model is to determine drag of a bicycle, the solving of complexcalculus equations might be warranted. In that case, a specializedcomplex calculus solver may be incorporated into the collection 910 toperhaps supplement or replace an existing equations solver. In oneembodiment, each solver is designed to solve for one or more outputmodel variables in a particular kind of analytics relationship. Forexample, there might be one or more equation solvers configured to solvefor unknowns in an equation. There might be one or more rules solversconfigured to apply rules to solve for unknowns. There might be one ormore constraints solvers configured to apply constraints to therebysolve for unknowns. Other types of solves might be, for example, asimulation solver which performs simulations using input data to therebyconstruct corresponding output data.

The solver framework 901 is configured to coordinate processing of oneor more or all of the specialized solvers in the collection 910 tothereby cause one or more output model variables to be solved for. Thesolver framework 901 is then configured to provide the solved for valuesto one or more other external components. For instance, referring toFIG. 2, the solver framework 901 may provide the model parameter valuesto the view portion 230 of the pipeline, so that the solving operationthereby affects how the view components execute to render a view item,or thereby affect other data that is associated with the view item. Asanother potential effect of solving, the model analytics themselvesmight be altered. For instance, as just one of many examples in whichthis might be implemented, the model might be authored with modifiablerules set so that, during a given solve, some rule(s) and/orconstraint(s) that are initially inactive become activated, and somethat are initially activated become inactivated. Equations can bemodified this way as well.

FIG. 10 illustrates a flowchart of a method 1000 for the solverframework 901 to coordinate processing amongst the specialized solversin the collection 910. The method 1000 of FIG. 10 will now be describedwith frequent reference to the solver environment 900 of FIG. 9.

The solver framework begins a solve operation by identifying which ofthe model parameters are input model variables (act 1001), and which ofthe model parameters are output model variables (act 1002), and byidentifying the model analytics that define the relationship between themodel parameters (act 1003). Given this information, the solverframework analyzes dependencies in the model parameters (act 1004). Evengiven a fixed set of model parameters, and given a fixed set of modelanalytics, the dependencies may change depending on which of the modelparameters are input model variables and which are output modelvariables. Accordingly, the system can infer a dependency graph eachtime a solve operation is performed using the identity of which modelparameters are input, and based on the model analytics. The user neednot specify the dependency graph for each solve. By evaluatingdependencies for every solve operation, the solver framework has theflexibility to solve for one set of one or more model variables duringone solve operation, and solve for another set of one or more modelvariables for the next solve operation. In the context of FIGS. 2through 5, that means greater flexibility for a user to specify what isinput and what is output by interfacing with the view composition.

In some solve operations, the model may not have any output modelvariables at all. In that case, the solve will verify that all of theknown model parameter values, taken together, satisfy all therelationships expressed by the analytics for that model. In other words,if you were to erase any one data value, turning it into an unknown, andthen solve, the value that was erased would be recomputed by the modeland would be the same as it was before. Thus, a model that is loaded canalready exist in solved form, and of course a model that has unknownsand gets solves now also exists in solved form. What is significant isthat a user interacting with a view of a solved model is neverthelessable to edit the view, which may have the effect of changing a datavalue or values, and thus cause a re-solve that will attempt torecompute data values for output model variables so that the new set ofdata values is consistent with the analytics. Which data values a usercan edit (whether or not a model starts with output model variables) iscontrolled by the author; in fact, this is controlled by the authordefining which variables represented permitted unknowns.

If there are expressions that have one or more unknowns that may beindependently solved without first solving for other unknowns in otherexpressions (Yes in decision block 1005), then those expressions may besolved at any time (act 1006), even perhaps in parallel with othersolving steps. On the other hand, if there are expressions that haveunknowns that cannot be solved without first solving for an unknown inanother expression, then a solve dependency has been found. In thatcase, the expression becomes part of a relational structure (such as adependency tree) that defines a specific order of operation with respectto another expression.

In the case of expressions that have interconnected solve dependenciesfrom other expressions, an order of execution of the specialized solversis determined based on the analyzed dependencies (act 1007). The solversare then executed in the determined order (act 1008). In one example, inthe case where the model analytics are expressed as equations,constraints, and rules, the order of execution may be as follows 1)equations with dependencies or that are not fully solvable as anindependent expression are rewritten as constraints 2) the constraintsare solved, 3) the equations are solved, and 4) the rules are solved.The rules solving may cause the data to be updated.

Once the solvers are executed in the designated order, it is thendetermined whether or not solving should stop (decision block 1009). Thesolving process should stop if, for example, all of the output modelvariables are solved for, or if it is determined that even though notall of the output model variables are solved for, the specializedsolvers can do nothing further to solve for any more of the output modelvariables. If the solving process should not end (No in decision block1009), the process returns back to the analyzing of dependencies (act1004). This time, however, the identity of the input and output modelvariables may have changed due to one or more output model variablesbeing solved for. On the other hand, if the solving process should end(Yes in decision block 1009) the solve ends (act 1010). However, if amodel cannot be fully solved because there are too many output modelvariables, the model nevertheless may succeed in generating a partialsolution where the output model variables have been assigned symbolicvalues reflective of how far the solve was able to proceed. For example,if a model has an equation A=B+C, and B is known to be “2” and is aninput model variable but C is an output model variable and A is also anoutput model variable and needs to be solved for, the model solvercannot product a numerical value for A since while B is known C isunknown; so instead of a full solve, the solver returns “2+C” as thevalue for A. It is thus clear to the author what additional variableneeds to become known, either by supplying it a value or by addingfurther rules/equations/constraints or simulations that can successfullyproduce the needed value from other input data.

This method 1000 may repeat each time the solver framework detects thatthere has been a change in the value of any of the known modelparameters, and/or each time the solver framework determines that theidentity of the known and unknown model parameters has changed. Solvingcan proceed in at least two ways. First, if a model can be fully solvedsymbolically (that is, if all equations, rules, and constraints can bealgorithmically rewritten so that a computable expression exists foreach unknown) then that is done, and then the model is computed. Inother words, data values are generated for each unknown, and/or datavalues that are permitted to be adjusted are adjusted. As a secondpossible way, if a model cannot be fully solved symbolically, it ispartially solved symbolically, and then it is determined if one or morenumerical methods can be used to effect the needed solution. Further, anoptimization step occurs such that even in the first case, it isdetermined whether use of numerical methods may be the faster way tocompute the needed values versus performing the symbolic solve method.Although the symbolic method can be faster, there are cases where asymbolic solve may perform so many term rewrites and/or so manyrewriting rules searches that it would be faster to abandon this andsolve using numeric methods.

FIG. 11 illustrates a solver environment 1100 that represents an exampleof the solver environment 900 of FIG. 9. In this case, the solvercoordination module 1110 acts to receive the input model variables 1011and coordinate the actions of the forward solver 1121, the symbolicsolver 1122 (or the “Inverter”), and the numeric solver 1123 such thatthe model variables 1102 (including the output model variables) aregenerated. The forward solver 1121, the symbolic solver 1122 and thenumeric solver 1123 are examples of the solvers that might be in thesolver collection 910 of FIG. 9.

The solver coordination module 1110 maintains a dependency graph of themodel analytics that have corresponding model variables. For each solveoperation, the solver coordination module 1110 may determine which ofthe model variables are input model variables, and which of the modelvariables are output model variables and thus are to be solved for.

The forward solver 1121 solves model analytics that are properlypresented so as to be forward solvable. For instance, if there is butone equation in the model analytics A=B+C, and if B and C are inputmodel variables, that A can be solved for using a forward solve byplugging in the values for B and C into the equation, and determiningthe resulting value for A.

The symbolic solver 1122 rewrites model analytics so as to be forwardsolvable. For instance, suppose in the equation A=B+C, it is variables Aand C that are input variables, and variable B that is an outputvariable to be solved for. In this situation, the model analytics cannotbe forward solved without first inverting the model analytics (in thiscase inverting the equation). Accordingly, the symbolic solver 1122rewrites the equation A=B+C as B=A−C. Now, the inverted equation can besubjected to a forward solve by the forward solver 1121 such that theinput variables A and C are plugged into the equation B=A−C to obtainthe value of variable B.

Some equations are not mathematically invertible, or at least it has notyet been discovered how to invert some types of equations. Furthermore,even if the equation is invertible, or it is known how to invert theequation, the symbolic solver 1122 might simply not be able to invertthe equation. Or perhaps it is simply inefficient for the symbolicsolver 1122 to invert the equation as compared to resorting to othersolving methods, such as numeric solving. Accordingly, the numericsolver 1123 is provided to solve model analytics using model analyticsin the case where the model analytics are not properly invertible(either because inversion was not possible, not known, or not enabled bythe symbolic solver).

The solver coordination module 1110 is configured to manage each solveoperation. For instance, FIG. 12 illustrates a flowchart of a method1200 for managing the solve operation such that model analytics may besolved for. The method 1200 may be managed by the solver environment 100under the direction of the solver coordination module 1110.

The solver coordination module 1110 identifies which of the modelvariables of the model analytics are input variable(s) for a particularsolve, and which of the model variables are output model variable(s) fora particular solve (act 1201). If, for example, the input and outputmodel variables are defined in FIG. 4 by the data-model binder component410, even given a constant set of model variables, the identity of theinput model variables and the output model variables may change from onesolve operation to the next. Accordingly, the coordination of the solveoperation may change from one solve operation to the next. For example,even given a constant set of model analytics, depending on the inputmodel variables, a forward solve may be sufficient for one solveoperation, an inversion and a forward solve of the inverted analyticsmay be sufficient for another solve operation, and perhaps a numericsolve may be sufficient for yet another solve operation.

However, if implemented in the context of the analytics portion 220 ofthe pipeline 201, even the model analytics may change as the modelanalytics are formulated or perhaps combined with other model analyticsas previously described. The solver environment 100 may account forthese changes by identifying the input and output model variableswhenever there is a change, by accounting for any changed modelanalytics, and solving appropriately.

For each solve, once the input and output model variables are identified(act 1201), the solver coordination module 1110 determines whether ornot a forward solve of the output parameter(s) is to be performed giventhe input model variables (s) without first inverting the modelanalytics (decision block 1202). If a forward solve is to be performed(Yes in decision block 1202), the forward solver 1121 is made to forwardsolve the model analytics (act 1203). This forward solve may be of theentire model analytics, or of only a portion of the model analytics. Inthe latter case, the method 1200 may be executed once again, only thistime with a more complete set of input model variables that include themodel variables solved for in the forward solve.

If it is determined that the forward solve of the output parameter(s) isnot to be performed for the particular solve at least not without firstinverting the model analytics (No in decision block 1202), it is thendetermined whether or not the model analytics is to be inverted for theparticular solve such that a forward solve may solve for the outputparameter(s) (decision block 1204). If the model analytics (or at leasta portion of the model analytics) is to be inverted (Yes in decisionblock 1204), the model analytics is inverted by the symbolic solver (act1205). Thereafter, the inverted model analytics may be solved for usinga forward solve (act 1203). Once again, if only a portion of the modelanalytics was solved for in this way, the method 1200 may be executedagain, but with an expanded set of input model variables.

If it is determined that the model analytics are not to be inverted forthe particular solve (No in decision block 1204), then the numericsolver may solve for the output variable(s) using numeric methods (act1206). Once again, if only a portion of the model analytics was solvedfor in this way, the method 1200 may be executed again, but with anexpanded set of input model variables.

Accordingly, a flexible solver environment 1200 has been described inwhich a wide variety of model analytics may be solved for regardless ofwhich model variables are input and which model variables are outputfrom one solve operation to the next.

Composite View Composition

The pipeline environment 200 also includes a model importation mechanism241 that is perhaps included as part of the authoring mechanism 240. Themodel importation mechanism 241 provides a user interface or otherassistance to the author to allow the author to import at least aportion of a pre-existing analytics-driven model into the currentanalytics-driven model that the user is constructing. Accordingly, theauthor need not always begin from scratch when authoring a new analyticsmodel. The importation may be of an entire analytics-driven model, orperhaps a portion of the model. For instance, the importation may causeone or more or all of the following six potential effects.

As a first potential effect of the importation, additional model inputdata may be added to the pipeline. For instance, referring to FIG. 2,additional data might be added to the input data 211, the analytics data221 and/or the view data 231. The additional model input data might alsoinclude additional connectors being added to the data access component310 of FIG. 3, or perhaps different canonicalization components 330.

As a second potential effect of the importation, there may be additionalor modified bindings between the model input data and the modelparameters. For instance, referring to FIG. 4, the data-model binder 410may cause additional bindings to occur between the canonicalized data401 and the model parameters 411. This may cause an increase in thenumber of known model parameters.

As a third potential effect of the importation, there may be additionalmodel parameters to generate a supplemental set of model parameters. Forinstance, referring to FIG. 4, the model parameters 411 may be augmenteddue to the importation of the analytical behaviors of the importedmodel.

As a fourth potential effect of the importation, there may be additionalanalytical relationships (such as equations, rules and constraints)added to the model. The additional input data resulting from the firstpotential effect, the additional bindings resulting for the secondpotential effect, the additional model parameters resulting from thethird potential effect, and the additional analytical relationshipsresulting from the fourth effect. Any one of more of these additionalitems may be viewed as additional data that affects the viewcomposition. Furthermore, any one or more of these effects could changethe behavior of the solver 440 of FIG. 4.

As a fifth potential effect of the importation, there may be additionalor different bindings between the model parameters and the inputparameters of the view. For instance, referring to FIG. 5, themodel-view binding component 510 binds a potentially augmented set ofmodel parameters 411 to a potentially augmented set of view componentsin the view component repository 520.

As a sixth potential effect of the importation, there may be additionalparameterized view components added to the view component repository 520of FIG. 5, resulting in perhaps new view items being added to the viewcomposition.

Accordingly, by importing all or a portion of another model, the dataassociated with that model is imported. Since the view composition isdata-driven, this means that the imported portions of the model areincorporated immediately into the current view composition.

When the portion of the pre-existing analytics-driven analytics model isimported, a change in data supplied to the pipeline 201 occurs, therebycausing the pipeline 201 to immediately, or in response to some otherevent, cause a regeneration of the view composition. Thus, upon what isessentially a copy and paste operation from an existing model, thatresulting composite model might be immediately viewable on the displaydue to a resolve operation.

As an example of how useful this feature might be, consider the FengShui room view composition of FIG. 6. The author of this application maybe a Feng Shui expert, and might want to just start from a standard roomlayout view composition model. Accordingly, by importing a pre-existingroom layout model, the Feng Shui expert is now relatively quickly, ifnot instantly, able to see the room layout 601 show up on the displayshown in FIG. 6. Not only that, but now the furniture and room itemcatalog that normally might come with the standard room layout viewcomposition model, has now become available to the Feng Shui applicationof FIG. 6.

Now, the Feng Shui expert might want to import a basic pie chart elementas a foundation for building the Feng Shui chart element 602. Now,however, the Feng Shui expert might specify specific fixed inputparameters for the chart element including perhaps that there are 8wedges total, and perhaps a background image and a title for each wedge.Now the Feng Shui expert need only specify the analytical relationshipsspecifying how the model parameters are interrelated. Specifically, thecolor, position, and type of furniture or other room item might have aneffect on a particular Feng Shui score. The expert can simply write downthose relationships, to thereby analytically interconnect the roomlayout 601 and the Feng Shui score. This type of collaborative abilityto build on the work of others may generate a tremendous wave ofcreativity in creating applications that solve problems and permitvisual analysis. This especially contrasts with systems that might allowa user to visually program a one-way data flow using a fixed dependencygraph. Those systems can do one-way solves, the way originallyprogrammed from input to output. The principles described herein allowsolves in multiple ways, depending on what is known and what is unknownat any time given the interactive session with the user.

Visual Interaction

The view composition process has been described until this point asbeing a single view composition being rendered at a time. For instance,FIG. 6 illustrates a single view composition generated from a set ofinput data However, the principles described herein can be extended toan example in which there is an integrated view composition thatincludes multiple constituent view compositions. This might be helpfulin a number of different circumstances.

For example, given a single set of input data, when the solver mechanismis solving for output model variables, there might be multiple possiblesolutions. The constituent view compositions might each represent one ofmultiple possible solutions, where another constituent view compositionmight represent another possible solution.

In another example, a user simply might want to retain a previous viewcomposition that was generated using a particular set of input data, andthen modify the input data to try a new scenario to thereby generate anew view composition. The user might then want to retain also thatsecond view composition, and try a third possible scenario by alteringthe input data once again. The user could then view the three scenariosat the same time, perhaps through a side-by-side comparison, to obtaininformation that might otherwise be difficult to obtain by just lookingat one view composition at a time.

FIG. 13 illustrates an integrated view composition 1300 that extendsfrom the Feng Shui example of FIG. 6. In the integrated viewcomposition, the first view composition 600 of FIG. 6 is representedonce again using elements 601 and 602, exactly as shown in FIG. 6.However, here, there is a second view composition that is emphasizedrepresented. The second view composition is similar to the first viewcomposition in the there are two elements, a room display and a FengShui score meter. However, the input data for the second viewcomposition was different than the input data for the first viewcomposition. For instance, in this case, the position data for severalof the items of furniture would be different thereby causing theirposition in the room layout 1301 of the second view composition to bedifferent than that of the room layout 601 of the first viewcomposition. However, the different position of the various furnitureitems correlates to different Feng Shui scores in the Feng Shui meter1302 of the second view composition as compared to the Feng Shui meter602 of the first view composition.

The integrated view composition may also include a comparison elementthat visually represents a comparison of a value of at least oneparameter across some of all of the previously created and presentlydisplayed view compositions. For instance, in FIG. 13, there might be abar graph showing perhaps the cost and delivery time for each of thedisplayed view compositions. Such a comparison element might be anadditional view component in the view component repositor 520. Perhapsthat comparison view element might only be rendered if there aremultiple view compositions being displayed. In that case, the comparisonview composition input parameters may be mapped to the model parametersfor different solving iterations of the model. For instance, thecomparison view composition input parameters might be mapped to the costparameter that was generated for both of the generations of the firstand second view compositions of FIG. 13, and mapped to the deliveryparameter that was generated for both of the generations of the firstand second view compositions.

Referring to FIG. 13, there is also a selection mechanism that allowsthe user to visually emphasize a selected subset of the total availablepreviously constructed view compositions. The selection mechanism 1310is illustrated as including three possible view constructions 1311, 1312and 1313, that are illustrated in thumbnail form, or are illustrated insome other deemphasized manner. Each thumbnail view composition 1311through 1313 includes a corresponding checkbox 1321 through 1323. Theuser might check the checkbox corresponding to any view composition thatis to be visually emphasized. In this case, the checkboxes 1321 and 1323are checked, thereby causing larger forms of the corresponding viewconstructions to be displayed.

The integrated view composition, or even any single view composition forthat matter, may have a mechanism for a user to interact with the viewcomposition to designate what model parameters should be treated as anunknown thereby triggering another solve by the analytical solvermechanism. For instance, in the room display 1301 of FIG. 13, one mightright click on a particular item of furniture, right click on aparticular parameter (e.g., position), and a drop down menu might appearallowing the user to designate that the parameter should be treated asunknown. The user might then right click on the harmony percentage(e.g., 95% in the Feng Shui score meter 1302), whereupon a slider mightappear (or a text box of other user input mechanism) that allows theuser to designate a different harmony percentage. Since this wouldresult in the identity of the known and unknown parameters beingchanged, a re-solve would result, and the item of furniture whoseposition was designated as an unknown might appear in a new location.

In one embodiment, the integrated view composition might also include avisual prompt for an adjustment that could be made that might trend avalue of a model parameter in a particular direction. For example, inthe Feng Shui example, if a particular harmony score is designated as aknown input parameter, various positions of the furniture might besuggested for that item of furniture whose position was designated as anunknown. For instance, perhaps several arrows might emanate from thefurniture suggesting a direction to move the furniture in order toobtain a higher harmony percentage, a different direction to move tomaximize the water score, a different direction to move to maximum thewater score, and so forth. The view component might also show shadowswhere the chair could be moved to increase a particular score. Thus, auser might use those visual prompts in order to improve the designaround a particular parameter desired to be optimized. In anotherexample, perhaps the user wants to reduce costs. The user might thendesignate the cost as an unknown to be minimized resulting in adifferent set of suggested furniture selections.

ADDITIONAL EXAMPLE APPLICATIONS

The architecture of FIGS. 1 and 2 may allow countless data-drivenanalytics model to be constructed, regardless of the domain. There isnothing at all That need be similar about these domains. Wherever thereis a problem to be solved where it might be helpful to apply analyticsto visuals, the principles described herein may be beneficial. Up untilnow, only a few example applications have been described including aFeng Shui room layout application. To demonstrate the wide-rangingapplicability of the principles described herein, several additionalwide-ranging example applications will now be described.

Additional Example #1 Retailer Shelf Arrangements

Product salespersons often use 3-D visualizations to sell retailers onshelf arrangements, end displays and new promotions. With the pipeline201, the salesperson will be able to do what-ifs on the spot. Given someproduct placements and given a minimum daily sales/linear footthreshold, the salesperson may calculate and visualize the minimumrequired stock at hand. Conversely, given some stock at hand and given abi-weekly replenishment cycle, the salesperson might calculate productplacements that will give the desired sales/linear foot. The retailerwill be able to visualize the impact, compare scenarios, and compareprofits. FIG. 14 illustrates an example retailer shelf arrangementvisualization. The input data might include visual images of theproduct, a number of the product, a linear square footage allocated foreach product, and shelf number for each product, and so forth.

Additional Example #2 Urban Planning

Urban planning mash ups are becoming prominent. Using the principlesdescribed herein, analytics can be integrated into such solutions. Acity planner will open a traffic model created by experts, and drag abridge in from a gallery of road improvements. The bridge will bringwith it analytical behavior like length constraints and high-windoperating limits. Via appropriate visualizations, the planner will seeand compare the effect on traffic of different bridge types andplacements. The principles described herein may be applied to any mapscenarios where the map might be for a wide variety of purposes. The mapmight be for understanding the features of a terrain and findingdirections to some location. The map might also be a visual backdrop forcomparing regionalized data. More recently, maps are being used tocreate virtual worlds in which buildings, interiors and arbitrary 2-D or3-D objects can be overlaid or positioned in the map. FIG. 15illustrates an example visualized urban plan.

Additional Example #3 Visual Education

In domains like science, medicine, and demographics where complex dataneeds to be understood not just by domain practitioners but also thepublic, authors can use the principles described herein to create datavisualizations that intrigue and engage the mass audience. They will usedomain-specific metaphors, and impart the authors' sense of style. FIG.16 is an illustration about children's education. FIG. 17 is aconventional illustration about population density. Conventionally, suchvisualizations are just static illustrations. With the principlesdescribed herein, these can become live, interactive experiences. Forinstance, by inputting a geographically distributed growth pattern asinput data, a user might see the population peaks change. Somevisualizations, where the authored model supports this, will let usersdo what-ifs. That is, the author may change some values and see theeffect on that change on other values.

Accordingly, the principles described herein provide a major paradigmshift in the world of visualized problem solving and analysis. Theparadigm shift applies across all domains as the principles describedherein may apply to any domain.

Domain-Specific Taxonomy of Data

Referring back to FIG. 2, the pipeline 201 is data-driven. For instance,input data 211 is provided to data portion 210, analytics data 221 isprovided to analytics portion 220, and view data 231 is provided to viewportion 230. Examples of each of these data have already been described.Suffice it to say that the volume of data that could be selected by theauthoring component 240 may be quite large, especially given the ease ofcomposition in which portions of models can be imported into a model tocompose more and more complex models. To assist in navigating throughthe data, so that the proper data 211, 221 and 231 may be selected, ataxonomy component 260 provides a number of domain-specific taxonomiesof the input data.

FIG. 18 illustrates a taxonomy environment 1800 in which the taxonomycomponent 260 may operate. Taxonomy involves the classification of itemsinto categories and the relating of those categories. The environment1800 thus includes a collection of items 1810 that are to be subjectedto taxonomization. In FIG. 18, the collection of items 1810 isillustrated as including only a few items altogether including items1811A through 1811P (referred to collectively as “member items 1811”).Although only a few member items 1811 are shown, there may be any numberof items, perhaps even hundreds, thousands or even millions of itemsthat should be categorized and taxonomized as represented by theellipsis 1811Q. The member items 1811 include the pool of member itemsfrom which the authoring component 240 may select in order to providethe data 211, 221 and 231 to the pipeline 201.

A domain-sensitive taxonomization component 1820 accesses all or aportion of the member items 1811, and also is capable of generating adistinct taxonomy of the member items 1811. For instance, thetaxonomization component 1820 generates domain specific taxonomies 1821.In this case, there are five domain-specific taxonomies 1821A through1821E, amongst potentially others as represented by the ellipsis 1821F.There may also be fewer than five domain-specific taxonomies created andmanaged by the taxonomization component 1820.

As an example, the taxonomy 1821A might taxonomize the member itemssuitable for a Feng Shui domain, taxonomy 1821B may taxonomize themember items suitable for a motorcycle design domain, taxonomy 1821C maylikewise be suitable for a city planning domain, taxonomy 1821D may besuitable for an inventory management domain, and taxonomy 1821E may besuitable for an abstract artwork domain. Of course, these are just fiveof the potentially countless number of domains that may be served by thepipeline 201. Each of the taxonomies may use all or a subset of theavailable member items to classify in the corresponding taxonomy.

FIG. 19 illustrates one specific and simple example 1900 of a taxonomyof the member items. For example, the taxonomy may be thedomain-specific taxonomy 1821A of FIG. 18. Subsequent figures will setforth more complicated examples. The taxonomy 1900 includes categorynode 1910 that includes all of the member items 1811 except member items1811A and 1811E. The category node 1910 may be an object that, forexample, includes pointers to the constituent member items and thus inthe logical sense, the member items may be considered “included within”the category node 1910. The category node 1910 also has associatedtherewith a properties correlation descriptor 1911 that describes themembership qualifications for the category node 1910 using theproperties of the candidate member items. When determining whether ornot a member item should be included in a category, the propertiescorrelation descriptor may be used to evaluate the descriptor againstthe properties of the member item.

In a taxonomy, two categories can be related to each other in a numberof different ways. One common relation is that one category is a subsetof another. For example, if there is a “vehicle” category that containsall objects that represent vehicles, there might be a “car” categorythat contains a subset of the vehicles category. The propertycorrelation descriptors of both categories may define the specificrelationship. For instance, the property correlation descriptor for thevehicles category may indicate that objects having the followingproperties will be included in the category: 1) the object is movable,2) the object may contain a human. The car category property correlationdescriptor may include these two property requirements either expresslyor implicitly, and may also include the following propertyrequirements: 1) the object contains at least 3 wheels that maintaincontact with the earth during motion of the object, 2) the object isautomotive, 3) the height of the object does not exceed 6 feet. Based onthe property correlation descriptors for each category, thetaxonomization component may assign an object to one or more categoriesin any given domain-specific taxonomy, and may also understand therelationship between the categories.

In FIG. 19, a second category node 1920 is shown that includes anotherproperty correlation descriptor 1921. The category node 1920 logicallyincludes all member items that satisfy the property correlationdescriptor 1921. In this case, the member items logically included inthe category node are a subset of the member items included in the firstcategory node 1910 (e.g. including member items 1811F, 1811J, 1811N and1811P). This could be because the property correlation descriptor 1921of the second category node 1920 specifies the same propertyrequirements as the correlation descriptor 1911 of the first categorynode 1910, except for one or more additional property requirements. Therelation between the first category node 1910 and the second categorynode 1920 is logically represented by relation 1915.

In a vehicle-car example, the relationship between the categories is asubset relation. That is, one category (e.g., the car category) is asubset of the other (e.g., the vehicle category). However, there are awide variety of other types of relationships as well, even perhaps newrelationships that have never been recognized or used before. Forexample, there might be a majority inheritance relationship in which ifa majority (or some specified percentage) of the objects in one categoryhave a particular property value, the objects in another category havethis property and inherit this property value. There might be a “similarcolor” relationship in which if one category of objects has a primarycolor within a certain wavelength range of visible light, then the othercategory contains objects having a primary color within a certainneighboring wavelength range of visible light. There might be a “virusmutation” relationship in which if one category contains objects thatrepresent certain infectious diseases that are caused primarily by aparticular virus, a related category might include objects thatrepresent certain infectious diseases that are caused by a mutated formof the virus. The examples could go on and on for volumes. One ofordinary skill in the art will recognize after having reviewed thisdescription, that the kinds of relationships between categories is notlimited.

Further a single taxonomy can have many different types of relations.For clarity, various taxonomies will now be described in an abstractsense. Examples of abstractly represented taxonomies are shown in FIGS.20A through 20C. Then specific examples will be described, understandingthat the principles described herein enable countless applications ofdomain-specific taxonomies in a data-driven visualization.

While the example of FIG. 19 is a simple two category node taxonomy, theexamples of FIGS. 20A through 20C are more complex. Each node in thetaxonomies 2000A through 2000C of FIGS. 20A through 20C represents acategory node that contains zero or more member items, and may have aproperty correlation descriptor associated with each that is essentiallyan admission policy for admitting member items into the category node.To avoid undue complexity, however, the member items and propertycorrelation descriptor for each of the category nodes of the taxonomies2000A through 2000C are not illustrated. The lines between the categorynodes represent the relations between category nodes. They might be asubset relation or some other kind of relation without limit. Theprecise nature of the relations between category nodes is not critical.Nevertheless, to emphasize that there may be a variety of relation typesbetween category nodes in the taxonomy, the relations are labeled withan A, B, C, D, or E.

FIGS. 20A through 20C are provided just as an example. The precisestructure of the taxonomies of FIGS. 20A through 20C is not only notcritical, but the principles described herein permit great flexibilityin what kinds of taxonomies can be generated even based on the same setof input candidate member items. In these examples, the taxonomy 2000Aincludes category node 2001A through 2010A related to each other usingrelation types A, B and C. Taxonomy 2000B includes categories 2001Bthrough 2008B related to each other using relation types B, C and D.Taxonomy 2000C includes categories 2001C through 2012C related to eachother using relation types C, D and E. In this example, taxonomies 2000Aand 2000B are hierarchical, whereas taxonomy 2000C is more of anon-hierarchical network.

As new candidate member items become available, those candidate memberitems may be evaluated against the property correlation descriptor ofeach of the category nodes in each the taxonomies. If the properties ofthe member item have values that permit the member item to satisfy therequirements of the property correlation descriptor (i.e., the admissionpolicy), the member item is admitted into the category node. Forinstance, perhaps a pointer to the member item is added to the categorynode. Thus, if new member items have sufficient numbers of properties,new member items may be imported automatically into appropriatecategories in all of the taxonomies.

FIG. 21 illustrates a member item 2100 that includes multiple properties2101. There might be a single property, but there might also bepotentially thousands of properties associated with the member item2100. In FIG. 21, the member item 2100 is illustrated as including fourproperties 2101A, 2101B, 2101C and 2101D, amongst potentially others asrepresented by the ellipsis 2101F. There is no limit to what theseproperties may be. They might be anything that might be useful incategorizing the member item into a taxonomy.

In one embodiment, potential data for each of the data portion 210, theanalytics portion 220, and the view portion 230 may be taxonomized. Forexample, consider the domain in which the author is composing a consumerapplication that allows an individual (such as a consumer orneighborhood resident) to interface with a map of a city.

In this consumer domain, there might be a taxonomy for view data 231that can be selected. For instance, there might be a building categorythat includes all of the buildings. There might be different types ofbuildings: government buildings, hospitals, restaurants, houses, and soforth. There might also be a transit category that includes railroad,roadways, and canals sub-categories. The roadways category might containcategories or objects representing streets, highways, bike-paths,overpasses, and so forth. The streets category might include objects orcategories of visual representations of one way streets, multi-linestreets, turn lanes, center lanes, and so forth. There might be aparking category showing different types of visual representations ofparking or other sub-categories of parking (e.g., multi-level parking,underground parking, street parking, parking lots, and so forth).Parking might also be sub-categorized by whether or not parking is free,or whether there is a cost.

There might also be a taxonomy of the input data in this consumerdomain. For instance, a parking structure might have data associatedwith it such as, for example, 1) whether the parking is valet parking,2) what the hourly change is for the parking, 3) the hours that theparking is open, 4) whether the parking is patrolled by security, and ifso, how many security officers there are per unit area of parking, 5)the number of levels of the parking, 6) the square footage of theparking if there is but one level, and if multi-level parking, thesquare footage on each level, 7) the annualized historical number of carthefts that occur in the parking structure, 8) the volume usage of theparking, 9) whether parking is restricted to the satisfaction of one ormore conditions (i.e., employment at a nearby business, patronage at arestaurant or mall, and so forth), or any other data that might behelpful. There might also be data associated with other visual items aswell, and data that may never affect how the visual items is rendered,but might be used for a calculation at some point.

However, there might also be a taxonomy of the analytics data 221 thatis specific to this consumer map domain. For instance, the analyticsmight present cost-based analytics in one category, time-based analyticsin another category, distance-based analytics in yet another category,directory analytics in another category, and routing analytics inanother category. Here, the analytics are taxonomized to assist theauthor in formulating an analytical model for the desired application.For instance, the routing analytics category might include a categoryfor equations that calculate a route, a constraint that specifies whatrestrictions can be made on the routing (such as shortest route, mostuse of highways, avoid streets, and so forth), or rules (such as trafficdirections on particular roads). Similar subcategories might also beincluded for the other categories as well.

Now consider another domain, also dealing with the layout of a city, butthis time, the domain is city planning. Here, there are analytics thatare interesting to city planners that are of little to no interest to aconsumer. For instance, there might be analytics that calculate howthick the pavement should be given a certain traffic usage, what theoverall installation and maintenance cost per linear foot of a certainroad type placed in a certain area, what the safety factor of a bridgeis given expected traffic patterns forecast for the next 20 years, whatthe traffic bottlenecks are in the current city plan, what theenvironmental impact would be if a particular building was constructedin a particular location, what the impact would be if certainrestrictions were placed on the usage of that particular building and soforth. Here, the problems to be solved are different than those to besolved in the consumer domain. Accordingly, the taxonomy of theanalytics may be laid out much differently for the city planning domainas compared to the consumer domain, even though both deal with a citytopology.

On the other hand, a tractor design domain might be interested in awhole different set of analytics, and would use a different taxonomy.For instance, the visual items of a tractor design domain might betotally different than that of a city planning domain. No longer isthere a concern for city visual elements. Now, the various visualelements that make up a tractor are taxonomized. As an example, thevisual items might be taxonomized using a relation of what can beconnected to what. For instance, there might be a category for “Thingsthat can be connected to the seat”, “Things that can be connected to thecarburetor, “Things that can be connected to the rear axle” and soforth. There might also be a different analytics taxonomy. For instance,there might be a constraint regarding tread depth on the tiresconsidering that the tractor needs to navigate through wet soil. Theremight be analytics that calculate the overall weight of the tractor orits subcomponents, and so forth.

FIG. 22 illustrates a domain-specific taxonomy 2200 and represents oneexample of the domain-specific taxonomies 1821 of FIG. 18. In oneembodiment, the domain-specific taxonomy includes a data taxonomy 2201in which at least some of the available data items are taxonomized intocorresponding related categories, a view component taxonomy 2203 inwhich at least some of the available view components are taxonomizedinto a corresponding related view components categories, and ananalytics taxonomy 2202 in which at least some of the availableanalytics are taxonomized into correlating related analytics categories.Examples of such domain specific taxonomies in which data, analytics,and view components are taxonomized in a manner that is specific todomain have already been described.

FIG. 23 illustrates a method for navigating and using analytics. Theanalytics component 220 is accessed (act 2301) along with thecorresponding domain specific analytics taxonomy (act 2303). If thereare multiple domain-specific analytics taxonomies, the domain may firstbe identified (act 2302), before the domain-specific analytics taxonomymay be accessed (act 2303).

Then, the analytics taxonomy may be navigated (act 2304) by traversingthe related categories. This navigation may be performed by a humanbeing with the assistance of a computing system, or may be performedeven by a computing system alone without the contemporaneous assistanceof a human being. A computer or human may derive information from thecorrelation property descriptor for each category that defines thatadmission policy for analytics to be entered into that category.Information may also be derived by the relationships between categories.The navigation may be used to solve the analytics problem therebysolving for output model parameters, or perhaps for purposes of merginganalytics from multiple models. Alternatively, the navigation may beused to compose the anaytics model in the first place.

For instance, suppose an analytics taxonomy categorizes relations interms of the identity of the type of problem to be solved. The composercould begin by reviewing all of those analytics in the problem typecategory of interest. That category might have related categories thatdefine portions of the problem to be solved. The user could quicklynavigate to those related categories and find analytics that are ofrelevance in the domain.

Search and Exploration

As previously mentioned, the data-driven analytics model may be used toform analytics intensive search and exploration operations. FIG. 24illustrates a flowchart of a method 2400 for searching using thedata-driven analytics model. The method 2400 may be performed each timethe search tool 242 receives or otherwise accesses a search request (act2401).

The principles described herein are not limited to the mechanism thatthe user may use to enter a search request. Nevertheless, a few exampleswill now be provided to show the wide diversity of search request entrymechanisms. In one example, perhaps the search request is text-based andentered directly into a text search field. In another example, perhapsradio buttons are filled in to enter search parameters. Perhaps a slidermight also be used to enter a range for the search parameter. The searchrequest generation may have been generated in an interactive fashionwith the user. For example, in the case where the user requests realestate that experiences a certain noise level range, the applicationmight generate noise that gets increasingly louder, and ask the user tohit the “Too Much Noise” button when the noise gets louder that the userwant to bear.

The search request is not a conventional search request, but may requiresolving operations of the data-driven analytics model. Before solving,however, the search tool 242 of FIG. 2 identifies any model parametersthat should be solved for in order to be able to respond to the request(act 2402). This might be accomplished using, for example, the varioustaxonomies discussed above. For instance, in the case where the user issearching for real estate that is not in the shadow of a mountain after9:15 at any point of the year, there might be a model variable called“mountain shade” that is solved for. In the case where the user searchesfor real estate that experiences certain noise levels, there might be amodel variable called “average noise” that is to be solved for given aparticular coordinate.

Once the relevant output variables are identified, the analyticalrelations of the analytics portion 220 are used to solve for the outputvariables (act 2403). The solved output variable(s) are then used by thesearch tool 242 to formulate a response to the search request using thesolved value(s) (act 2404). Although in some cases the user mightinteract with the method 2400 as the method 2400 is being executed, themethod 2400 may be performed by a computing system withoutcontemporaneous assistance from a human being. The search request may beissued by a user or perhaps even by another computing or softwaremodule.

The method 2400 may be repeated numerous times each time a searchrequest is processed. The model variables that are solved for may, butneed not, be different for each search request. For example, there maybe three search requests for homes that have a certain price range, andnoise levels. For example, there might be a search request for homes inthe $400,000 to $600,000 price range, and whose average noise levels arebelow 50 decibels. The parameters to be solved for here would be thenoise levels. A second search request may be for homes in the $200,000to $500,000 price range, and whose noise levels are below 60 decibels.Here the parameters to be solved for would once again be noise levels.Note, however, that in the second search request, some of the solvingoperations would have already been done for the search request. Forinstance, based on the first search request, the system alreadyidentified homes in the $400,000 to $500,000 price range whose noiselevels were below 50 decibels. Thus, for those houses, there is no needto recalculate the noise levels. Once solved, those values may be keptfor future searching. Thus, this allows a user to perform exploration bysubmitting follow-on requests. The user might then submit a third searchrequest for houses in the $400,000 to $500,000 price range, and havingnoise levels less than 45 decibels. Since noise levels for those homeshave already been solved for, there is no need to solve for them again.Accordingly, the search results can be returned with much lesscomputation. In essence, the system may learn new information by solvingproblems, and be able to take advantage of that new information to solveother problems.

As previously mentioned, each search request might involve solving fordifferent output model variables. For instance, after performing thesearch requests just described, the user might submit a search requestfor houses that are not in the shadow of a mountain. Once the systemsolves for this, whenever this or another user submits a similarrequest, the results from the solve may be used to fulfill thatsubsequent search request. A user might submit a search request forhouses that would stay standing in a magnitude 8.0 earthquake, causing asimulation to verify that each house would either stay standing, fall,or perhaps provide some percentage chance that the house would remainstanding. For houses for which there was insufficient structuralinformation to perform an accurate simulation, the system might simplystate that the results are inconclusive. Once the earthquake simulationsare performed, unless there was some structural change to the house thatrequired re-simulation, or unless there was some improved simulationsolver, the results may be used whenever someone submits a searchrequests for houses that could withstand a certain magnitude ofearthquake. A user might also perform a search request for houses withina certain price range that would not be flooded or destroyed should acategory 5 hurricane occur.

Having described the embodiments in some detail, as a side-note, thevarious operations and structures described herein may, but need not, beimplemented by way of a computing system. Accordingly, to conclude thisdescription, an example computing system will be described with respectto FIG. 25.

FIG. 25 illustrates a computing system 2500. Computing systems are nowincreasingly taking a wide variety of forms. Computing systems may, forexample, be handheld devices, appliances, laptop computers, desktopcomputers, mainframes, distributed computing systems, or even devicesthat have not conventionally been considered a computing system. In thisdescription and in the claims, the term “computing system” is definedbroadly as including any device or system (or combination thereof) thatincludes at least one processor, and a memory capable of having thereoncomputer-executable instructions that may be executed by the processor.The memory may take any form and may depend on the nature and form ofthe computing system. A computing system may be distributed over anetwork environment and may include multiple constituent computingsystems.

As illustrated in FIG. 25, in its most basic configuration, a computingsystem 2500 typically includes at least one processing unit 2502 andmemory 2504. The memory 2504 may be physical system memory, which may bevolatile, non-volatile, or some combination of the two. The term“memory” may also be used herein to refer to non-volatile mass storagesuch as physical storage media. If the computing system is distributed,the processing, memory and/or storage capability may be distributed aswell. As used herein, the term “module” or “component” can refer tosoftware objects or routines that execute on the computing system. Thedifferent components, modules, engines, and services described hereinmay be implemented as objects or processes that execute on the computingsystem (e.g., as separate threads).

In the description that follows, embodiments are described withreference to acts that are performed by one or more computing systems.If such acts are implemented in software, one or more processors of theassociated computing system that performs the act direct the operationof the computing system in response to having executedcomputer-executable instructions. An example of such an operationinvolves the manipulation of data. The computer-executable instructions(and the manipulated data) may be stored in the memory 2504 of thecomputing system 2500.

Computing system 2500 may also contain communication channels 2508 thatallow the computing system 2500 to communicate with other messageprocessors over, for example, network 2510. Communication channels 2508are examples of communications media. Communications media typicallyembody computer-readable instructions, data structures, program modules,or other data in a modulated data signal such as a carrier wave or othertransport mechanism and include any information-delivery media. By wayof example, and not limitation, communications media include wiredmedia, such as wired networks and direct-wired connections, and wirelessmedia such as acoustic, radio, infrared, and other wireless media. Theterm computer-readable media as used herein includes both storage mediaand communications media.

Embodiments within the scope of the present invention also includecomputer-readable media for carrying or having computer-executableinstructions or data structures stored thereon. Such computer-readablemedia can be any available media that can be accessed by a generalpurpose or special purpose computer. By way of example, and notlimitation, such computer-readable media can comprise physical storageand/or memory media such as RAM, ROM, EEPROM, CD-ROM or other opticaldisk storage, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to carry or store desired programcode means in the form of computer-executable instructions or datastructures and which can be accessed by a general purpose or specialpurpose computer. When information is transferred or provided over anetwork or another communications connection (either hardwired,wireless, or a combination of hardwired or wireless) to a computer, thecomputer properly views the connection as a computer-readable medium.Thus, any such connection is properly termed a computer-readable medium.Combinations of the above should also be included within the scope ofcomputer-readable media.

Computer-executable instructions comprise, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Although the subject matter has been described inlanguage specific to structural features and/or methodological acts, itis to be understood that the subject matter defined in the appendedclaims is not necessarily limited to the specific features or actsdescribed herein. Rather, the specific features and acts describedherein are disclosed as example forms of implementing the claims.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. The scope of the invention is, therefore, indicatedby the appended claims rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

1. A method for searching using a data-driven analytics model thatincludes at least an analytical modeling component that definesanalytical relationships between the plurality of model variables usinga plurality of analytical relations, wherein the analytical modelingcomponent uses the plurality of analytical relations to identify whichof the plurality of model variables is or are to be solved for, andwhich of the plurality of mode variables is or are an input variable,wherein an identity of the output model variable(s) and an identity ofthe input model variable(s) may change from one solve operation to thenext, the method comprising: an act of accessing a search request; anact of identifying at least one output variable of the plurality ofmodel variables using the search request; an act of using the pluralityof analytical relations to solve for the identified at least one outputvariable; and an act of formulating a response to the search requestusing the solved value(s) of the at least one output variable.
 2. Themethod in accordance with claim 1, wherein the search request is onesearch request (a “first” search request), the method furthercomprising: an act of receiving another search request (a “second”search request); an act of identifying at least one output variable ofthe plurality of model variables using the second search request; an actof using the plurality of analytical relations to solve for theidentified at least one output variable that was identified using thesecond search request; and an act of formulating a response to thesecond search request using the solved value(s) of the at least oneoutput variable that was identified using the second search request. 3.The method in accordance with claim 2, further comprising: an act ofreceiving yet another search request (a “third” search request); an actof identifying at least one output variable of the plurality of modelvariables using the third search request; an act of using the pluralityof analytical relations to solve for the identified at least one outputvariable that was identified using the third search request; and an actof formulating a response to the third search request using the solvedvalue(s) of the at least one output variable that was identified usingthe third search request.
 4. The method in accordance with claim 3,wherein the identity of the at least one output variable identifiedusing the third search request is different than the identity of the atleast one output variable identified using the first search request andis different than the identity of the at least one output variableidentified using the second search request.
 5. The method in accordancewith claim 2, wherein the identity of the at least one output variableidentified using the second search request is different than theidentity of the at least one output variable identified using the firstsearch request.
 6. The method in accordance with claim 2, wherein theidentity of the at least one output variable identified using the secondsearch request is the same as the identity of the at least one outputvariable identified using the first search request.
 7. The method inaccordance with claim 2, wherein the solved value(s) used to formulate aresponse to the first request are used as at least one input variablewhen using the plurality of analytical relations to solve for theidentified at least one output variable that was identified using thesecond search request.
 8. The method in accordance with claim 1, whereinat least some of the plurality of analytical relations are equations. 9.The method in accordance with claim 1, wherein at least some of theplurality of analytical relations are constraints.
 10. The method inaccordance with claim 1, wherein at least some of the plurality ofanalytical relations are rules.
 11. The method in accordance with claim1, wherein at least some of the plurality of analytical relations aresimulations.
 12. The method in accordance with claim 1, wherein themethod is performed by a computing system without contemporaneousassistance from a human being.
 13. The method in accordance with claim1, wherein the data-driven analytics model further comprises: a datasource that is a source for a plurality of data items; and a data-modelbinding component configured to bind portions of the plurality of dataitems to corresponding model parameters of the plurality of modelparameters.
 14. The method in accordance with claim 1, wherein thedata-driven analytics model further comprises: a view componentsrepository that is capable of containing a plurality of heterogenic viewcomponents, each corresponding to a visual item that may be displayed,and at least some of which being parameterized; a model-visual bindingcomponent configured to bind the plurality of model parameter values toparameter(s) of at least one of a parameterized view componentscontained within the view components repository; and a view compositionmodule configured to formulate instructions for rendering a view thatcontains at least some of the visual items corresponding to theplurality of heterogenic view components.
 15. The method in accordancewith claim 1, wherein the search request requires a noise calculation inorder to formulate a response to the search request.
 16. The method inaccordance with claim 1, wherein the search request specifies a rangefor a value of a model variable that is not yet solved, where thenot-yet solved for variable becomes an output model variable for a solvethat is performed in order to satisfy the search request.
 17. The methodin accordance with claim 1, wherein the act of identifying at least oneoutput variable of the plurality of model variables using the searchrequest is performed using a taxonomy of the analytical relations.
 18. Acomputer program product comprising one or more computer-readable mediahaving thereon one or more computer-executable instructions that, whenexecuted by one or more processors of a computing system, cause thecomputing system to perform a method for searching using a data-drivenanalytics model that includes at least an analytical modeling componentthat defines analytical relationships between the plurality of modelvariables using a plurality of analytical relations, wherein theanalytical modeling component uses the plurality of analytical relationsto identify which of the plurality of model variables is or are to besolved for, and which of the plurality of mode variables is or are aninput variable, wherein an identity of the output model variable(s) andan identity of the input model variable(s) may change from one solveoperation to the next, the method comprising: an act of identifying atleast one output variable of the plurality of model variables using asearch request received from a user; an act of using the plurality ofanalytical relations to solve for the identified at least one outputvariable; and an act of formulating a response to the search requestusing at least one solved value of the at least one output variable. 19.A computer program product in accordance with claim 18, wherein thecomprising one or more computer-readable media is physical storagemedia.
 20. A method for searching and exploring search results using adata-driven analytics model that includes at least an analyticalmodeling component that defines analytical relations between theplurality of model variables using a plurality of analytical relations,wherein an identity of the output model variable(s) and an identity ofthe input model variable(s) may change from one solve operation to thenext, the method comprising: an act of accessing a first search request;an act of identifying at least one output variable of the plurality ofmodel variables using the first search request; an act of using theplurality of analytical relations to solve for the identified at leastone output variable identified using the first search request; an act offormulating a response to the search request using the solved value(s)of the at least one output variable identified using the first searchrequest; an act of receiving a second search request; an act ofidentifying at least one output variable of the plurality of modelvariables using the second search request; an act of using the pluralityof analytical relations and the solved value(s) of the at least oneoutput variable identified using the first search request to solve forthe identified at least one output variable that was identified usingthe second search request; and an act of formulating a response to thesecond search request using the solved value(s) of the at least oneoutput variable that was identified using the second search request.